{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib图鉴——基础柱状图\n",
    "\n",
    "## 公众号：可视化图鉴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3.2\n",
      "1.2.0\n",
      "1.19.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.__version__) #查看Matplotlib版本\n",
    "import pandas as pd\n",
    "print(pd.__version__) #查看pandas版本\n",
    "import numpy as np\n",
    "print(np.__version__) #查看numpy版本\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  #设置中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，代码在以下环境全部通过测试:\n",
    "- Python 3.7.1\n",
    "- Matplotlib == 3.0.2\n",
    "- pandas == 0.23.4\n",
    "- numpy == 1.15.4\n",
    "\n",
    "因版本不同，可能会有部分语法差异，如有报错，请先检查拼写及版本是否一致！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础柱状图——分组柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(list(range(7)))\n",
    "y1 = [7,6,5,4,3,2,1]\n",
    "y2 = [6,2,3,3,1,1,0.5]\n",
    "\n",
    "width = 0.4\n",
    "x = x - 0.2\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "plt.bar(x, y1,  width=width, label='系列1',edgecolor = 'black',alpha = 0.7)\n",
    "plt.bar(x + width, y2, width=width, label='系列2',edgecolor = 'black')\n",
    "\n",
    "plt.xticks(x + 0.2,['周一', '周二', '周三', '周四', '周五', '周六', '周日'])\n",
    "\n",
    "\n",
    "plt.legend(loc=1,fontsize=13)  # 设置图例位置\n",
    "plt.ylabel('我是Y轴',fontsize=15)\n",
    "plt.xlabel('我是X轴',fontsize=15)\n",
    "plt.title(\"基础柱状图——分组柱状图\",fontsize=17)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础柱状图——分组柱状图——添加数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(list(range(7)))\n",
    "y1 = [7,5,5,4,3,2,1]\n",
    "y2 = [6,2,3,3,1,1,0.5]\n",
    "\n",
    "width = 0.4\n",
    "x = x - 0.2\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "plt.bar(x, y1,  width=width, label='系列1',edgecolor = 'black',alpha = 0.7)\n",
    "plt.bar(x + width, y2, width=width, label='系列2',edgecolor = 'black')\n",
    "\n",
    "plt.xticks(x + 0.2,['周一', '周二', '周三', '周四', '周五', '周六', '周日'])\n",
    "\n",
    "plt.ylim(0, 8)\n",
    "#使用plt.text添加数值\n",
    "for a, b in zip(x,y1):\n",
    "    plt.text(a, b+0.1, b, ha='center', va='bottom',fontsize = 14)\n",
    "    \n",
    "for a,b in zip(x,y2):\n",
    "    plt.text(a+width, b+0.1, b, ha='center', va='bottom',fontsize = 14)\n",
    "\n",
    "plt.legend(loc=1,fontsize=13)  # 设置图例位置\n",
    "plt.ylabel('我是Y轴',fontsize=15)\n",
    "plt.xlabel('我是X轴',fontsize=15)\n",
    "plt.title(\"基础柱状图——分组柱状图\",fontsize=17)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础柱状图——分组柱状图——水平、展示数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5,6,7])\n",
    "y1 = [7,5,3,4,3,2,1]\n",
    "y2 = [6,2,6,3,1,1,0.5]\n",
    "names = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']\n",
    "width = 0.4\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9,6),dpi = 100)\n",
    "ax.barh(x, y1, width, label='系列1',edgecolor = 'black',alpha = 0.7)\n",
    "ax.barh(x + width, y2, width, label='系列2',edgecolor = 'black',color = 'deeppink',alpha = 0.7)\n",
    "\n",
    "ax.set(yticks=x + 0.2, yticklabels=names, ylim=[0.5, 8])\n",
    "\n",
    "ax.set_title(\"基础柱状图——分组柱状图——水平\",fontsize = 16)\n",
    "ax.set_xlabel('我是X轴',fontsize=15)\n",
    "ax.set_ylabel('我是Y轴',fontsize=15)\n",
    "ax.legend(fontsize = 14)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(x)):\n",
    "    \n",
    "    plt.text(y1[i]+0.1,x[i]-0.1,y1[i],fontsize = 12)\n",
    "    \n",
    "    plt.text(y2[i]+0.1,x[i]+0.3,y2[i],fontsize = 12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础柱状图—分组柱状图—官方示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "men_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2)\n",
    "women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3)\n",
    "\n",
    "ind = np.arange(len(men_means))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, men_means, width, yerr=men_std,\n",
    "                color='SkyBlue', label='Men')\n",
    "rects2 = ax.bar(ind + width/2, women_means, width, yerr=women_std,\n",
    "                color='IndianRed', label='Women')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Scores')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def autolabel(rects, xpos='center'):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar in *rects*, displaying its height.\n",
    "\n",
    "    *xpos* indicates which side to place the text w.r.t. the center of\n",
    "    the bar. It can be one of the following {'center', 'right', 'left'}.\n",
    "    \"\"\"\n",
    "\n",
    "    xpos = xpos.lower()  # normalize the case of the parameter\n",
    "    ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n",
    "    offset = {'center': 0.5, 'right': 0.57, 'left': 0.43}  # x_txt = x + w*off\n",
    "\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()*offset[xpos], 1.01*height,\n",
    "                '{}'.format(height), ha=ha[xpos], va='bottom')\n",
    "\n",
    "\n",
    "autolabel(rects1, \"left\")\n",
    "autolabel(rects2, \"right\")\n",
    "\n",
    "plt.show()"
   ]
  }
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